MI.local: Local Moran Coefficient

In spfilteR: Semiparametric Spatial Filtering with Eigenvectors in (Generalized) Linear Models

Local Moran Coefficient

Description

Reports the local Moran Coefficient for each unit.

Usage

MI.local(x, W, alternative = "greater", na.rm = TRUE)

Arguments

x	a vector
W	spatial connectivity matrix
alternative	specification of alternative hypothesis as 'greater' (default), 'lower', or 'two.sided'
na.rm	listwise deletion of observations with missing values (TRUE/ FALSE)

Value

Returns an object of class data.frame that contains the following information for each variable:

Ii

observed value of local Moran's I

EIi

expected value of local Moran coefficients

VarIi

variance of local Moran's I

zIi

standardized local Moran coefficient

pIi

p-value of the test statistic

Note

The calculation of the statistic and its moments follows Anselin (1995) and Sokal et al. (1998).

Author(s)

Sebastian Juhl

References

Anselin, Luc (1991): Local Indicators of Spatial Association-LISA. Geographical Analysis, 27 (2): pp. 93 - 115.

Bivand, Roger S. and David W. S. Wong (2018): Comparing Implementations of Global and Local Indicators of Spatial Association. TEST, 27: pp. 716 - 748.

Sokal, Robert R., Neal L. Oden, Barbara A. Thomson (1998): Local Spatial Autocorrelation in a Biological Model. Geographical Analysis, 30 (4): pp. 331 - 354.

See Also

MI.vec, MI.ev, MI.sf, MI.resid, MI.decomp

Examples

data(fakedata)
x <- fakedataset$x2

(MIi <- MI.local(x = x, W = W, alternative = "greater"))

spfilteR documentation built on April 4, 2025, 1:12 a.m.